A Rank-Switching, Open-Row DRAM Controller for Time-Predictable Systems
Bibliographic record
Abstract
We introduce ROC, a Rank-switching, Open-row Controller for Double Data Rate Dynamic RAM (DDR DRAM). ROC is optimized for mixed-criticality multicore systems using modern DDR devices: compared to existing real-time memory controllers, it provides significantly lower worst case latency bounds for hard real-time tasks and supports throughput-oriented optimizations for soft real-time applications. The key to improved performance is an innovative rank-switching mechanism which hides the latency of write-read transitions in DRAM devices without requiring unpredictable request reordering. We further employ open row policy to take advantage of the data caching mechanism (row buffering) in each device. ROC provides complete timing isolation between hard and soft tasks and allows for compositional timing analysis over the number of cores and memory ranks in the system. We implemented and synthesized the ROC back end in Verilog RTL, and evaluated its performance on both synthetic tasks and a set of representative benchmarks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".